Distance Metric. We empirically observed that Mahalanobis distance (M. Dist) outperformed Euclidean (also reported in [1]}), Manhattan, and Chebyshev distances. Unlike [2]}, who use M. Dist with label-smoothing and [1]}, with shared covariance, we observed M. Dist to perform best with class-wise covariance matrices and without label-smoothing.
We found the shared covariance assumption more restrictive and did not fit different types of data. From Table REF , we see that it performs worse across all tasks and domains compared to our M. Dist formulation (Section REF ).
We also see that using only our M. Dist follows the same trend as TRUST-LAPSE regarding semantic sensitivity, i.e., it is more sensitive to semantic content than dataset statistics, but its performance is not high enough to be competitive.
